000 04205nam a22005415i 4500
001 978-3-662-48838-6
003 DE-He213
005 20220801220937.0
007 cr nn 008mamaa
008 160219s2016 gw | s |||| 0|eng d
020 _a9783662488386
_9978-3-662-48838-6
024 7 _a10.1007/978-3-662-48838-6
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aMachine Learning for Cyber Physical Systems
_h[electronic resource] :
_bSelected papers from the International Conference ML4CPS 2015 /
_cedited by Oliver Niggemann, Jürgen Beyerer.
250 _a1st ed. 2016.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer Vieweg,
_c2016.
300 _aVI, 121 p. 12 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,
_x2522-8587
505 0 _aDevelopment of a Cyber-Physical System based on selective dynamic Gaussian naive Bayes model for a self-predict laser surface heat treatment process control -- Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks -- Forecasting Cellular Connectivity for Cyber- Physical Systems: A Machine Learning Approach -- Towards Optimized Machine Operations by Cloud Integrated Condition Estimation -- Prognostics Health  Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission -- Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases -- Towards a novel learning assistant for networked automation systems -- Effcient Image Processing System for an Industrial Machine Learning Task -- Efficient engineering in special purpose machinery through automated control code synthesis based on a functional categorisation -- Geo-Distributed Analytics for the Internet of Things -- Imple mentation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation -- Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency -- Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems -- Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems.
520 _aThe work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
650 0 _aComputational intelligence.
_97716
650 0 _aData mining.
_93907
650 0 _aKnowledge management.
_912739
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aData Mining and Knowledge Discovery.
_953260
650 2 4 _aKnowledge Management.
_912739
700 1 _aNiggemann, Oliver.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_953261
700 1 _aBeyerer, Jürgen.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_953262
710 2 _aSpringerLink (Online service)
_953263
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783662488362
776 0 8 _iPrinted edition:
_z9783662488379
830 0 _aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,
_x2522-8587
_953264
856 4 0 _uhttps://doi.org/10.1007/978-3-662-48838-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79116
_d79116